code: def do_train(learning_rate=0.001, dropout=0.1, conv1=32, conv2=64):
      model = get_model(**locals())
  
      model.fit(
          x_train,
          y_train,
          batch_size=batch_size,
          epochs=num_epochs,
          verbose=1,
          validation_split=0.5,
      )
  
      res = model.evaluate(
          x_test,
          y_test,
          batch_size=batch_size,
          verbose=1,
      )
  
      # register the saving model function
      # add model as argument
      aup_save_model(save_model, model)
  
      flops = get_flops()
  
      return (res[1] - 1.0) / log(flops)
  
file_path: auptimizer/Examples/mnist_keras_save_model/mnist.py
imports: from aup import print_result, aup_args, aup_save_model
  import tensorflow
  import numpy
  from tensorflow import keras
  from tensorflow.keras import layers
  from math import log
  import sys
label_desc: Use image node_id 3 for calling a global function with example usage: do_train(learning_rate, dropout, conv1, conv2) and returns: unknown